VRM [1]} is a training principle that draws additional virtual examples from the vicinity distribution (neighborhood around each example in the training data) of the training set to enlarge the training distribution. Data augmentation is a common type of VRM method. However, it is not a general VRM method in the sense that the design of data augmentation requires expert knowledge and is dataset dependent.
